Gastos Casa
11 julio, 2022
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(.,format = "html", format.args = list(decimal.mark = ",", big.mark = "."),
caption="Tabla 1. Gastos Casa (últimos 30 registros)", align =rep('c', 3)) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
kableExtra::scroll_box(width = "100%", height = "300px")| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 18/5/2022 | Comida | 41.970 | Tami | NA |
| 19/5/2022 | VTR | 21.990 | Andrés | NA |
| 21/5/2022 | Pila estufa | 6.414 | Andrés | Pila cr2 switchbot |
| 25/5/2022 | Reloj | 8.914 | Andrés | Reloj alarma temperatura y humedad |
| 28/5/2022 | Comida | 138.918 | Tami | Wild Foods |
| 29/5/2022 | Parafina | 42.490 | Tami | NA |
| 30/5/2022 | Comida | 50.346 | Tami | NA |
| 30/5/2022 | Netflix | 8.320 | Tami | NA |
| 1/6/2022 | Diosi | 7.000 | Andrés | Pilas collar |
| 3/6/2022 | Electricidad | 24.792 | Andrés | Pac enel 01686518 |
| 6/6/2022 | Enceres | 19.400 | Tami | Caja Papel Higiénico |
| 7/6/2022 | Comida | 15.260 | Andrés | NA |
| 7/6/2022 | Comida | 23.450 | Andrés | NA |
| 13/6/2022 | Comida | 57.775 | Tami | NA |
| 18/6/2022 | Gas | 81.350 | Andrés | NA |
| 19/6/2022 | VTR | 21.990 | Andrés | NA |
| 20/6/2022 | Electricidad | 67.655 | Andrés | NA |
| 21/6/2022 | Comida | 38.000 | Andrés | NA |
| 21/6/2022 | Comida | 15.000 | Andrés | Flor de loto verduras |
| 24/6/2022 | Comida | 40.400 | Andrés | Bar la Providencia |
| 27/6/2022 | Agua | 12.502 | Andrés | PAC AGUAS ANDIN 000000005687837 |
| 29/6/2022 | Netflix | 8.320 | Tami | NA |
| 29/6/2022 | Comida | 68.213 | Tami | NA |
| 30/6/2022 | Comida | 15.310 | Tami | NA |
| 30/6/2022 | Electricidad | 67.655 | Andrés | NA |
| 2/7/2022 | Diosi | 35.990 | Andrés | NA |
| 3/7/2022 | Gas | 19.600 | Andrés | NA |
| 3/7/2022 | Parafina | 44.029 | Tami | NA |
| 31/3/2019 | Comida | 9.000 | Andrés | NA |
| 8/9/2019 | Comida | 24.588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 4.1966e+08 2 4.6221 0.0103 *
## lag_depvar 7.3925e+10 1 1628.3998 <2e-16 ***
## Residuals 2.0883e+10 460
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 821.1395 13636.54 0.0224588
## 2-0 27964.529 22025.2426 33903.82 0.0000000
## 2-1 20735.691 17107.1037 24364.28 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 268 47160.57 2 51426.86
## 269 51907.43 2 47160.57
## 270 49751.43 2 51907.43
## 271 54407.43 2 49751.43
## 272 54746.29 2 54407.43
## 273 61634.57 2 54746.29
## 274 58926.43 2 61634.57
## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 308 50198.79 16479.064
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2073.932251 4063.959746 -561.451127 2417.803844 -3015.862883
## 7 8 9 10 11
## 501.942310 -5680.554827 -1159.802629 -3935.211434 -357.629982
## 12 13 14 15 16
## -4887.974435 -1521.455103 -812.347756 457.552636 -3181.537709
## 17 18 19 20 21
## -297.948779 -2061.573476 6679.617400 -1532.261697 -1201.242710
## 22 23 24 25 26
## 1488.409566 -1194.748820 233.960071 1687.103707 -7130.356511
## 27 28 29 30 31
## 986.577149 8212.430041 351.623560 -81.118195 -2464.012447
## 32 33 34 35 36
## 1539.072504 4519.908945 1031.851570 2292.590941 -1982.388444
## 37 38 39 40 41
## 4521.142722 4252.571958 -2362.140220 -3035.347979 -1128.821091
## 42 43 44 45 46
## -10747.440940 7387.579945 2572.153463 1354.710587 8080.928181
## 47 48 49 50 51
## 586.185912 6434.372917 6568.385366 -6075.245264 -4907.506417
## 52 53 54 55 56
## -5111.664363 -7925.402375 6208.867699 -4067.234949 -4847.119695
## 57 58 59 60 61
## 3943.974566 927.145060 -6.783442 164.243877 -4978.993153
## 62 63 64 65 66
## 18189.929630 3520.095158 -3787.059842 5837.110617 7209.284313
## 67 68 69 70 71
## 14449.803089 1386.130222 -13497.682246 -1427.552985 4549.774502
## 72 73 74 75 76
## -5027.437042 -4468.509823 -10510.979393 2556.563154 -5345.459416
## 77 78 79 80 81
## 1163.338934 -6789.823808 680.055355 -2243.810284 -2574.504838
## 82 83 84 85 86
## -3801.703933 -385.876634 2452.052602 3859.951343 524.941125
## 87 88 89 90 91
## -447.656661 233.099537 4331.468029 -1179.415745 1147.382630
## 92 93 94 95 96
## -2079.093515 -1037.436617 193.308268 286.390750 -7476.973653
## 97 98 99 100 101
## 2470.316444 -8557.420634 -2817.769744 -3903.703410 -1578.922042
## 102 103 104 105 106
## -1106.671541 3328.211654 -2244.620644 2701.617505 -1089.983009
## 107 108 109 110 111
## 1041.157876 2639.053164 -3134.547533 -4675.394466 -762.993089
## 112 113 114 115 116
## 1987.727263 11748.253083 -1308.962292 2622.243813 4196.080774
## 117 118 119 120 121
## 3402.542758 -1221.830697 -4812.447340 -3762.501140 2322.147390
## 122 123 124 125 126
## -1753.447944 1338.800147 8843.472814 747.701620 35.033379
## 127 128 129 130 131
## -2606.228211 2605.034253 6982.653132 882.461640 -8623.160371
## 132 133 134 135 136
## 1723.389656 4095.588526 -3239.426599 -1455.258193 -871.558454
## 137 138 139 140 141
## -3887.406362 1213.992857 -480.287727 -2895.858857 1761.670223
## 142 143 144 145 146
## -1860.140327 -7793.063942 2146.949076 -3406.010588 2200.093530
## 147 148 149 150 151
## -192.844396 1081.577361 -318.529175 1390.888713 1206.855691
## 152 153 154 155 156
## 3362.274914 -4889.897027 -1152.113563 -3205.235499 6014.537831
## 157 158 159 160 161
## 9738.785256 -3142.223002 -4469.841174 3942.130437 477.020537
## 162 163 164 165 166
## 2962.672246 -5682.480303 -6468.691108 4488.077244 17662.529503
## 167 168 169 170 171
## 3706.699275 -346.087528 -2375.810591 -997.732337 3714.993233
## 172 173 174 175 176
## -135.548578 -7971.944465 3055.989537 4484.570073 740.690432
## 177 178 179 180 181
## 8864.658346 -9217.762290 -3331.023769 -10566.483184 -10955.469484
## 182 183 184 185 186
## 1617.674750 9635.120131 -1206.285481 6158.188912 6713.970544
## 187 188 189 190 191
## 13247.049690 8388.524407 -4172.436012 2426.208212 10324.140170
## 192 193 194 195 196
## -1776.083750 -2528.368887 -10312.992704 -6268.033081 1400.334436
## 197 198 199 200 201
## -5083.060307 -9592.100562 5682.966396 -2844.183710 -1465.797414
## 202 203 204 205 206
## -552.064628 6741.423608 10045.648584 631.068258 2980.114862
## 207 208 209 210 211
## 3131.070449 5796.253721 12798.417741 -5839.051740 -11351.877025
## 212 213 214 215 216
## -5577.685728 -10431.738634 -4807.986902 1833.265922 -12740.041320
## 217 218 219 220 221
## 16783.566964 7996.804663 1624.973470 26772.931517 12327.647156
## 222 223 224 225 226
## 7040.328505 13704.260902 -4332.355970 -2050.231946 3541.195200
## 227 228 229 230 231
## 129.171752 2557.537806 8828.174785 5598.867760 -2150.815638
## 232 233 234 235 236
## -2002.693191 9310.799087 -11691.872306 -7303.447623 -8463.445653
## 237 238 239 240 241
## -9924.747859 3357.886995 1582.476048 -8091.944790 -8703.113823
## 242 243 244 245 246
## 9459.945759 -7525.457918 2798.524315 -10037.758997 -3694.870927
## 247 248 249 250 251
## 1798.305956 1333.922482 -12019.875639 4052.630914 2398.449246
## 252 253 254 255 256
## 4500.263307 2357.984940 -972.174753 11332.746041 20943.804807
## 257 258 259 260 261
## 3035.427526 -4434.944467 4026.932804 -1797.934618 3679.784337
## 262 263 264 265 266
## -4928.555877 -10892.652254 -4592.583012 -334.938873 -5003.448663
## 267 268 269 270 271
## 9012.491613 -4158.293060 4357.968700 -1992.036678 4568.860672
## 272 273 274 275 276
## 793.987730 7382.881944 -1411.289302 12054.301678 -4683.362623
## 277 278 279 280 281
## 1701.533741 -400.750002 7843.001327 -5142.202376 -2734.640151
## 282 283 284 285 286
## -11219.040121 -2487.522470 18860.156052 7761.884594 2637.455738
## 287 288 289 290 291
## -732.811702 834.324057 6337.363702 6766.924989 -18941.609644
## 292 293 294 295 296
## -11051.911728 -7896.817403 9975.575908 3247.506184 -1045.570841
## 297 298 299 300 301
## 27548.727011 9882.358299 4633.443995 9238.172274 2510.974680
## 302 303 304 305 306
## -1356.053861 7638.937339 -24601.426854 -3493.986933 -79.036719
## 307 308 309 310 311
## -6863.730160 -3777.500623 3169.739879 -9000.422200 -2930.528623
## 312 313 314 315 316
## -7863.617354 1969.166746 -2795.693912 2418.302816 -3764.159048
## 317 318 319 320 321
## 27792.832809 -758.961429 3283.072713 10796.677634 5440.158207
## 322 323 324 325 326
## 32195.302766 4565.751997 -21461.400828 1608.049957 943.489142
## 327 328 329 330 331
## -6608.232842 -1762.401942 -33252.934350 1362.899547 -1863.603223
## 332 333 334 335 336
## 347.567697 -2753.415191 4514.987924 -86.559410 -6614.726801
## 337 338 339 340 341
## -2707.340046 -1767.791357 -7254.475084 4347.285824 -960.759799
## 342 343 344 345 346
## -1335.882931 -594.711079 563.912456 843.306440 -1283.776870
## 347 348 349 350 351
## -9109.172462 -12770.682890 2887.488296 -3822.076753 -3139.361532
## 352 353 354 355 356
## -5453.303502 2313.622512 1879.007327 3191.548813 -3396.195716
## 357 358 359 360 361
## -121.791107 1051.523076 7354.348348 504.895327 178.874183
## 362 363 364 365 366
## 2794.618543 -2577.549487 -668.074005 -8525.701748 -4295.857973
## 367 368 369 370 371
## -5837.028846 -4513.166513 -6779.325217 5550.790560 797.600176
## 372 373 374 375 376
## 7511.510536 -7365.142703 -1900.737964 -3016.016306 -2071.460393
## 377 378 379 380 381
## -12053.182121 2447.239567 -10157.569059 6279.322246 9794.890290
## 382 383 384 385 386
## 3425.274969 -2157.499892 1868.866378 6974.982352 11542.293276
## 387 388 389 390 391
## -5823.042505 -5289.299604 -4.834606 8717.422672 1852.604171
## 392 393 394 395 396
## 11246.813057 -9992.419281 2820.214876 731.837515 585.902394
## 397 398 399 400 401
## -624.198623 -511.507371 -14417.195597 8804.222908 -1023.930930
## 402 403 404 405 406
## -1196.345204 7177.253104 -7834.420266 -1080.143453 -2298.985927
## 407 408 409 410 411
## -5555.086605 -2521.431409 -3555.187824 -8357.756974 6628.255484
## 412 413 414 415 416
## 2015.350365 -7043.485499 -7277.674302 14715.020351 4074.550437
## 417 418 419 420 421
## 4683.885490 -7911.515442 -4503.450420 -2300.373914 3144.524104
## 422 423 424 425 426
## -13740.581770 -2337.078668 -8635.611980 3570.489027 7449.920255
## 427 428 429 430 431
## 6916.946832 -3756.986422 -3841.103880 -4396.219254 -1414.182764
## 432 433 434 435 436
## -5333.233385 -6191.416017 -5451.997218 -850.067071 -327.070354
## 437 438 439 440 441
## -4481.921847 3107.034463 5289.124035 -4705.352207 -1757.073811
## 442 443 444 445 446
## 1982.012470 -3478.856001 3226.436943 -6250.070630 -11708.657485
## 447 448 449 450 451
## -3969.037667 10208.104377 -1643.533244 5148.006249 -5563.662278
## 452 453 454 455 456
## -749.497664 751.979517 3369.470838 -11983.208029 3810.009948
## 457 458 459 460 461
## -6334.289485 6959.919051 3333.819078 2773.433148 -3621.166134
## 462 463 464 465
## 2366.496055 229.594087 2025.750381 -316.458664
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17195.35 20075.04 24377.59 24092.34 26472.58 23774.77 24499.27 19676.95
## 10 11 12 13 14 15 16 17
## 19410.50 16722.92 17509.26 14201.31 14253.06 14925.30 16641.25 14942.09
## 18 19 20 21 22 23 24 25
## 15988.57 15354.95 22518.26 21591.81 21065.73 22977.32 22295.61 22955.61
## 26 27 28 29 30 31 32 33
## 24822.64 18681.71 20427.57 28354.38 28412.69 28081.87 25684.21 27102.66
## 34 35 36 37 38 39 40 41
## 30989.58 31341.98 32767.25 30249.43 34190.43 37435.14 34457.63 31232.11
## 42 43 44 45 46 47 48 49
## 30066.73 20538.71 28143.28 30607.58 31709.21 38625.39 38114.20 42829.61
## 50 51 52 53 54 55 56 57
## 47114.25 39728.79 34235.24 29201.12 22267.28 28629.09 25170.69 21426.03
## 58 59 60 61 62 63 64 65
## 25884.71 27158.64 27459.04 27875.56 23699.36 40480.05 42345.06 37536.75
## 66 67 68 69 70 71 72 73
## 41791.72 46763.48 57553.44 55544.54 40619.27 38096.65 41149.01 35384.08
## 74 75 76 77 78 79 80 81
## 30784.41 21381.72 24619.75 20498.95 22608.82 17446.09 19484.52 18702.22
## 82 83 84 85 86 87 88 89
## 17718.85 15765.73 17058.09 20707.33 25175.49 26176.66 26201.90 26825.67
## 90 91 92 93 94 95 96 97
## 30997.84 29815.05 30825.81 28868.15 28058.83 28431.18 28842.40 22346.54
## 98 99 100 101 102 103 104 105
## 25395.99 18346.91 17189.99 15208.35 15511.53 16196.65 20720.33 19793.38
## 106 107 108 109 110 111 112 113
## 23344.55 23132.13 24827.38 27736.98 25206.54 21609.42 21887.99 24564.46
## 114 115 116 117 118 119 120 121
## 35552.96 33725.18 35583.63 38616.17 40594.40 38256.45 33018.36 29318.00
## 122 123 124 125 126 127 128 129
## 31424.59 29684.91 30879.96 38566.44 38204.82 37255.66 34083.39 35884.92
## 130 131 132 133 134 135 136 137
## 41344.40 40778.30 31879.61 33158.84 36385.00 32754.69 31123.56 30198.12
## 138 139 140 141 142 143 144 145
## 26715.86 28146.43 27913.43 25573.33 27620.85 26229.92 19759.05 22824.15
## 146 147 148 149 150 151 152 153
## 20626.05 23637.13 24183.28 25791.81 25975.97 27649.00 28964.58 32031.33
## 154 155 156 157 158 159 160 161
## 27449.83 26704.38 24231.75 30193.07 41162.65 39473.84 36808.73 41886.27
## 162 163 164 165 166 167 168 169
## 43310.90 46765.77 42179.98 37433.64 42920.76 59408.87 61646.23 60042.24
## 170 171 172 173 174 175 176 177
## 56831.73 55212.72 57946.12 56959.09 49163.30 52019.00 55804.31 55840.91
## 178 179 180 181 182 183 184 185
## 63051.05 53445.02 50158.91 40862.76 32305.61 35853.88 46072.57 45522.38
## 186 187 188 189 190 191 192 193
## 51543.03 57353.52 68259.48 73602.58 67225.36 67421.00 74571.94 70199.08
## 194 195 196 197 198 199 200 201
## 65670.85 54792.03 48754.09 50194.63 45739.10 37818.61 44316.61 42523.80
## 202 203 204 205 206 207 208 209
## 42157.64 42641.43 49512.92 58503.50 58128.89 59873.36 61548.03 65382.44
## 210 211 212 213 214 215 216 217
## 74956.91 66949.45 55003.83 49551.17 40444.84 37367.88 40517.04 30423.43
## 218 219 220 221 222 223 224 225
## 47590.48 54994.74 55906.93 78931.92 86512.39 88538.45 96216.36 87064.09
## 226 227 228 229 230 231 232 233
## 80994.09 80571.26 77183.03 76334.97 81125.99 82505.82 76877.84 72036.20
## 234 235 236 237 238 239 240 241
## 77754.30 64249.88 56195.59 48054.46 39570.40 43810.10 45987.37 39363.40
## 242 243 244 245 246 247 248 249
## 32970.91 43370.60 37551.90 41532.47 33708.16 32399.27 36096.22 38952.30
## 250 251 252 253 254 255 256 257
## 29677.23 35682.98 39527.74 44781.73 47531.03 47017.83 57436.20 75132.86
## 258 259 260 261 262 263 264 265
## 74945.80 68180.21 69678.93 65856.64 67319.27 61005.80 50158.15 46140.22
## 266 267 268 269 270 271 272 273
## 46352.02 42414.37 51318.86 47549.46 51743.47 49838.57 53952.30 54251.69
## 274 275 276 277 278 279 280 281
## 60337.72 57944.98 67728.22 61583.75 61796.18 60126.43 65934.77 59593.78
## 282 283 284 285 286 287 288 289
## 56118.47 45551.67 43930.13 61358.83 66951.97 67366.10 64754.25 63831.21
## 290 291 292 293 294 295 296 297
## 67877.79 71832.61 52612.48 42601.67 36544.42 46983.49 50262.29 49366.13
## 298 299 300 301 302 303 304 305
## 73838.36 79851.56 80526.83 85191.88 83369.91 78343.49 81849.86 56462.42
## 306 307 308 309 310 311 312 313
## 52680.89 52357.02 46076.36 43253.97 46898.42 39365.67 38073.19 32572.69
## 314 315 316 317 318 319 320 321
## 36400.41 35572.41 39447.59 37409.02 63489.53 61306.07 62948.18 71037.56
## 322 323 324 325 326 327 328 329
## 73452.13 99224.53 97583.69 73138.09 71922.23 70260.80 62120.69 59210.08
## 330 331 332 333 334 335 336 337
## 28815.53 32545.17 32989.72 35336.13 34669.44 40502.27 41590.16 36783.48
## 338 339 340 341 342 343 344 345
## 35988.93 36117.05 31382.57 37450.05 38121.03 38382.43 39268.23 41074.55
## 346 347 348 349 350 351 352 353
## 42917.35 42666.17 35530.25 25990.37 31396.08 30244.08 29829.45 27418.66
## 354 355 356 357 358 359 360 361
## 32150.99 35948.17 40462.77 38631.08 39905.76 42068.65 49548.39 50105.27
## 362 363 364 365 366 367 368 369
## 50309.24 52800.55 50255.22 49693.42 42254.57 39419.31 35552.60 33305.90
## 370 371 372 373 374 375 376 377
## 29318.64 36689.83 39002.92 46978.57 40881.31 40322.16 38842.75 38370.18
## 378 379 380 381 382 383 384 385
## 29133.47 33784.14 26756.39 35069.68 45520.87 49127.07 47380.71 49395.16
## 386 387 388 389 390 391 392 393
## 55686.42 65280.33 58414.01 52818.98 52544.58 60008.54 60537.90 69305.70
## 394 395 396 397 398 399 400 401
## 58286.79 59871.59 59426.67 58904.63 57374.22 56121.62 42728.78 51412.65
## 402 403 404 405 406 407 408 409
## 50401.63 49356.03 55830.56 48287.71 47590.99 45898.52 41526.29 40343.62
## 410 411 412 413 414 415 416 417
## 38385.33 32411.89 40374.79 43334.63 37945.96 32977.98 48019.88 51908.69
## 418 419 420 421 422 423 424 425
## 55882.94 48265.88 44547.09 43207.90 46835.44 35121.94 34848.04 29041.08
## 426 427 428 429 430 431 432 433
## 34694.94 43117.91 50088.99 46817.39 43852.50 40742.47 40629.38 37066.84
## 434 435 436 437 438 439 440 441
## 33161.00 30363.35 31957.50 33828.06 31809.82 36731.73 43008.35 39723.50
## 442 443 444 445 446 447 448 449
## 39426.13 42467.00 40328.85 44364.07 39556.51 30486.04 29310.18 40797.25
## 450 451 452 453 454 455 456 457
## 40475.14 46191.09 41777.21 42130.88 43769.96 47530.78 37288.99 42193.86
## 458 459 460 461 462 463 464 465
## 37564.65 45220.47 48780.85 51431.45 48123.50 50491.12 50694.96 52462.03
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8582
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.622095 0.5638473 2.909791
## t2* 1628.399814 30.3099002 246.683205
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.313797 4.739932 10.55653
## 2 lag_depvar 1287.404308 1638.559457 2091.10531Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Jul 11 00:36:17 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Jul 11 00:36:24 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Jul 11 00:36:31 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Jul 11 00:36:38 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Jul 11 00:36:45 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Jul 11 00:36:52 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Jul 11 00:36:58 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Jul 11 00:37:05 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Jul 11 00:37:12 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Jul 11 00:37:19 2022
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3) %>%
knitr::kable(format="html", caption="Tabla. Gastos promedio por ítem a contar del...",
col.names= c("Item","2023","2022","2021","2020")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "375px")| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | NA | 7.328667 | 7.328667 | 7.780500 |
| Comida | NA | 304.551500 | 304.551500 | 345.242400 |
| Comunicaciones | NA | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | NA | 37.112000 | 37.112000 | 27.473433 |
| Enceres | NA | 10.915000 | 10.915000 | 23.708267 |
| Farmacia | NA | 3.663333 | 3.663333 | 11.945800 |
| Gas/Bencina | NA | 54.006667 | 54.006667 | 23.138133 |
| Diosi | NA | 13.517833 | 13.517833 | 38.627233 |
| donaciones/regalos | NA | 0.000000 | 0.000000 | 9.157300 |
| Electrodomésticos/ Mantención casa | NA | 7.888000 | 7.888000 | 27.648933 |
| VTR | NA | 28.990000 | 28.990000 | 21.078267 |
| Netflix | NA | 7.369500 | 7.369500 | 7.584300 |
| Otros | NA | 6.302167 | 6.302167 | 1.260433 |
| Total | 0 | 481.644667 | 481.644667 | 544.645000 |
## Joining, by = "word"
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: 35 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:1651, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2022-07-09 00:04:58 sería de: 34.576 pesos// Percentil 95% más alto proyectado: 37.593,51
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="html", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "375px")## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 33418.81 | 33372.22 |
| Lo.80 | 33576.76 | 33604.30 |
| Point.Forecast | 34576.35 | 37139.05 |
| Hi.80 | 36243.66 | 41866.86 |
| Hi.95 | 37158.59 | 44369.61 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.3280 985.7562
## s.e. 0.1535 38.4750
##
## sigma^2 = 29191: log likelihood = -267.98
## AIC=541.96 AICc=542.61 BIC=547.1paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 xreg
## 0.3560 33.7281
## s.e. 0.1481 1.3248
##
## sigma^2 = 27491: log likelihood = -266.76
## AIC=539.52 AICc=540.17 BIC=544.67forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="html", caption="Tabla. Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "375px")## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 905.6835 | 631.2725 | 664.1008 |
| Lo.80 | 1026.0534 | 753.9717 | 743.6058 |
| Point.Forecast | 1253.4378 | 985.7558 | 920.6852 |
| Hi.80 | 1480.8221 | 1217.5400 | 1219.2415 |
| Hi.95 | 1601.1921 | 1340.2391 | 1414.6897 |
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.2.7 bsts_0.9.8 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.10 MASS_7.3-54 scales_1.2.0
## [7] ggiraph_0.8.2 tidytext_0.3.3 DT_0.23
## [10] autoplotly_0.1.4 rvest_1.0.2 plotly_4.10.0
## [13] xts_0.12.1 forecast_8.16 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.0 tm_0.7-8
## [19] NLP_0.2-1 tsibble_1.1.1 forcats_0.5.1
## [22] dplyr_1.0.9 purrr_0.3.4 tidyr_1.2.0
## [25] tibble_3.1.7 ggplot2_3.3.6 tidyverse_1.3.1
## [28] sjPlot_2.8.10 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.7.7 httr_1.4.3
## [34] readxl_1.4.0 zoo_1.8-10 stringr_1.4.0
## [37] stringi_1.7.6 DataExplorer_0.8.2 data.table_1.14.2
## [40] reshape2_1.4.4 fUnitRoots_3042.79 fBasics_3042.89.2
## [43] timeSeries_3062.100 timeDate_3043.102 plyr_1.8.7
## [46] readr_2.1.2
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.1.2 lme4_1.1-30
## [4] htmlwidgets_1.5.4 munsell_0.5.0 codetools_0.2-18
## [7] effectsize_0.7.0 its.analysis_1.6.0 withr_2.5.0
## [10] colorspace_2.0-3 ggfortify_0.4.14 highr_0.9
## [13] knitr_1.39 uuid_1.1-0 rstudioapi_0.13
## [16] TTR_0.24.3 labeling_0.4.2 emmeans_1.7.5
## [19] slam_0.1-50 bit64_4.0.5 farver_2.1.1
## [22] datawizard_0.4.1 rprojroot_2.0.3 vctrs_0.4.1
## [25] generics_0.1.3 xfun_0.31 R6_2.5.1
## [28] bitops_1.0-7 assertthat_0.2.1 networkD3_0.4
## [31] vroom_1.5.7 nnet_7.3-16 gtable_0.3.0
## [34] spatial_7.3-14 rlang_1.0.3 forge_0.2.0
## [37] systemfonts_1.0.4 splines_4.1.2 lazyeval_0.2.2
## [40] selectr_0.4-2 broom_1.0.0 yaml_2.3.5
## [43] abind_1.4-5 modelr_0.1.8 crosstalk_1.2.0
## [46] backports_1.4.1 quantmod_0.4.20 tokenizers_0.2.1
## [49] tools_4.1.2 ellipsis_0.3.2 gplots_3.1.3
## [52] kableExtra_1.3.4 jquerylib_0.1.4 Rcpp_1.0.9
## [55] base64enc_0.1-3 fracdiff_1.5-1 haven_2.5.0
## [58] fs_1.5.2 magrittr_2.0.3 lmtest_0.9-40
## [61] reprex_2.0.1 mvtnorm_1.1-3 sjmisc_2.8.9
## [64] hms_1.1.1 evaluate_0.15 xtable_1.8-4
## [67] sjstats_0.18.1 ggeffects_1.1.2 compiler_4.1.2
## [70] KernSmooth_2.23-20 crayon_1.5.1 minqa_1.2.4
## [73] htmltools_0.5.2 tzdb_0.3.0 lubridate_1.8.0
## [76] DBI_1.1.3 sjlabelled_1.2.0 dbplyr_2.2.1
## [79] boot_1.3-28 Matrix_1.3-4 car_3.1-0
## [82] cli_3.3.0 quadprog_1.5-8 parallel_4.1.2
## [85] insight_0.18.0 igraph_1.3.2 pkgconfig_2.0.3
## [88] xml2_1.3.3 svglite_2.1.0 bslib_0.3.1
## [91] webshot_0.5.3 estimability_1.4 anytime_0.3.9
## [94] snakecase_0.11.0 janeaustenr_0.1.5 digest_0.6.29
## [97] parameters_0.18.1 janitor_2.1.0 rmarkdown_2.14
## [100] cellranger_1.1.0 curl_4.3.2 gtools_3.9.2.2
## [103] urca_1.3-0 nloptr_2.0.3 lifecycle_1.0.1
## [106] nlme_3.1-153 jsonlite_1.8.0 tseries_0.10-51
## [109] carData_3.0-5 viridisLite_0.4.0 fansi_1.0.3
## [112] pillar_1.7.0 fastmap_1.1.0 glue_1.6.2
## [115] bayestestR_0.12.1 bit_4.0.4 sass_0.4.1
## [118] performance_0.9.1 r2d3_0.2.6 caTools_1.18.2#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Paquetes estadísticos utilizados')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({'font-size': '80%'});",
"}")))